The Gilt tech team doesn’t need an in-house psychic to help us predict which customers will buy products we’ve never sold before. Instead, we rely on the data wizardry performed by our Principal Data Scientist, Igor Elbert, who has been helping us to refine our product performance predictions (say that three times fast) by using various machine learning and predictive modeling techniques. Recently SearchCIO.com spoke to Igor about his ongoing work, which enables us to predict where products will sell better–and preemptively ship those products to reduce transit time. Here’s an excerpt:
How is the problem Gilt is trying to solve different from what Amazon calls ‘anticipatory shipping?’
Elbert: We kind of envy Amazon, because their problem is much easier. If you predicted for toothpaste in Orange County, California, you have a reliable past history of toothpaste sales. If it’s more or less stable, you can bet people next month will need the same amount of toothpaste they bought from you last month. Retailers have been doing this since forever – looking at sales forecasts and moving items closer to the customer. But Amazon took it one step further. They said, 'Knowing your previous purchase history, we’ll send the product to your doorstep.’ The model relies on, from what I understand, knowing what you bought before. So, if you bought toothpaste last month, and you’ve been buying toothpaste every two months for the last two years, they know you’ll need toothpaste next month and they can ship it to you. It’s a low risk to them because if you don’t need it, you can return it, but it’s likely you’ll actually need it.
We don’t go that far. We’re not going to ship a high-end dress to someone [before she’s bought it]. But we try to move products in the direction of the intended buyer early on.
Read more here. And don’t miss Part II of Igor’s interview with SearchCIO: “Mechanical Turk supplies Gilt with 'artificial artificial intelligence.’”
If you’re attending Strata + Hadoop World, you can hear Igor talk about his work in person when he presents a talk on predictive shipping (Friday, October 17 at 11:50 am).